Out of Alignment? Limitations of the Global Burden of Disease in Assessing the Allocation of Global Health Aid
Bibliographic record
Abstract
The Global Burden of Disease (GBD) project quantifies the impact of different health conditions by combining information about morbidity and premature mortality within a single metric, the Disability Adjusted Life Year. One important goal for the GBD project has been to inform decisions about global health priorities. A number of recent studies have used GBD data to argue that global health funding fails to align with the GBD. We argue that these studies' shared assumption that global health resources should 'align' with the burden of disease is unfounded and has troubling implications. First, since the allocation of resources involves difficult trade-offs between different, potentially competing goals, any 'misalignment' of allocation and disease burdens need not necessarily indicate that the allocation of funds fails to meet recipient countries' needs or interests. Second, using alignment as a baseline implicitly makes controversial assumptions about how harms of different magnitudes affecting different numbers of individuals should be aggregated. We discuss two alternative ways in which GBD data could help inform decisions about resource allocation, neither of which gives more than a limited role to GBD data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.266 | 0.412 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.014 | 0.030 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".